“The woman in the background”: Gendered Nouns in CNN and FOX Media Discourse
Bibliographic record
Abstract
Linguistic democratization, the goal or practice of increasing social equity through language, has not figured prominently in corpus studies. However, corpus-based approaches present the opportunity to probe questions of unequal linguistic representation on a large scale, providing crucial insights into how actors are classified in public discourse, especially with respect to the representation of gender relations and inequity. This paper draws on corpus methods to analyze the patterning of two generic, gendered nouns— woman and man—in American news television discourse. Results of both quantitative and qualitative analyses show that patterns for both grammatical factors (syntactic function, determiner type, pre-modification) and collocational behavior are largely consistent across networks, suggesting that gender ideologies expressed by newscasters and talk show hosts on both networks are not substantially different from one another. This study shows how elements of discourse that may be considered innocuous and below the level of consciousness—such as the position of certain nouns in the sentence, the determiners that specify them, and the adjectives that modify them—can provide valuable diagnostics of discourse-level democratization, and reveal deeper sociocultural ideologies about gendered individuals that are regularly perpetuated in public news discourse, regardless of the networks’ own political positioning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".